Lesson 1Consistency models: strong, causal, eventual, read-your-writes, monotonic readsDig into distributed consistency models, like strong, causal, an eventual consistency, plus read-your-writes an monotonic reads, explaining guarantees, anomalies, an how apps pick models dat match user expectations.
Strong consistency guaranteesEventual consistency and convergenceCausal consistency and orderingRead-your-writes and monotonic readsChoosing models for applicationsLesson 2Distributed consensus algorithms: Paxos, Raft, and practical implementations (etcd, Consul)Bring yuh to Paxos an Raft consensus algorithms an dem role in leader election, log replication, an configuration changes, den link theory to practice thru systems like etcd an Consul use fi metadata, locks, an coordination.
Consensus problem and safety goalsPaxos algorithm core ideasRaft algorithm and log replicationCluster membership and reconfigurationUsing etcd and Consul in practiceLesson 3Sharding and partitioning strategies: range, hash, and directory-basedSpell out sharding an partitioning strategies, including range, hash, an directory-based schemes, focusing pon data distribution, hotspot avoidance, rebalancing, an routing, an how to choose an evolve a strategy as workloads an data grow.
Range-based partitioning designHash-based sharding and hashingDirectory and lookup-based routingRebalancing and resharding methodsAvoiding hotspots and skewed keysLesson 4Replication models: leader-follower, multi-leader, and leaderless patternsCover leader-follower, multi-leader, an leaderless replication, explaining write an read paths, failure handling, lag, an conflict resolution, an how each model affect latency, throughput, durability, an operational complexity in global setups.
Leader-follower replication flowsMulti-leader replication and conflictsLeaderless quorum-based replicationReplication lag and read consistencyOperational trade-offs of each modelLesson 5CAP theorem and trade-offs between consistency, availability, and partition toleranceDig into di CAP theorem an its implications fi distributed databases, clearing up how consistency, availability, an partition tolerance interact, an how real systems navigate trade-offs using practical design patterns an service-level goals.
Formal statement of the CAP theoremConsistency vs availability in practicePartition tolerance in real networksDesigning around CAP with SLAsLesson 6Network partitions, latency, and failure modes across WAN linksCheck how network partitions, latency, an failures show up across WAN links, covering timeouts, partial failures, an split-brain, an how to design detection, retries, an degradation strategies dat keep systems predictable under stress.
Characteristics of WAN linksDetecting partitions and timeoutsHandling partial and asymmetric failuresSplit-brain risks and mitigationGraceful degradation strategiesLesson 7Idempotency, retries, and at-least-once vs exactly-once semanticsExplain idempotency an its role in safe retries, distinguishing at-least-once, at-most-once, an exactly-once semantics, an showing patterns fi deduplication, request tracking, an message processing in unreliable distributed environments.
Defining idempotent operationsDesigning safe retry mechanismsAt-least-once vs at-most-onceExactly-once semantics limitationsDeduplication and request trackingLesson 8Concurrency control: optimistic vs pessimistic, MVCC, conflict resolution techniquesLook at concurrency control in distributed databases, contrasting optimistic an pessimistic approaches, explaining MVCC internals, an presenting conflict detection an resolution techniques dat preserve correctness while enabling high concurrency.
Pessimistic locking in distributed systemsOptimistic control and validationMVCC snapshots and version chainsConflict detection and resolutionDeadlocks, timeouts, and retriesLesson 9Physical topology patterns: single region, active-passive, active-active, and hybridDescribe physical deployment topologies fi distributed databases, including single region, active-passive, active-active, an hybrid patterns, an analyze dem impact pon latency, failover behavior, data consistency, an operational complexity.
Single-region deployment trade-offsActive-passive failover patternsActive-active multi-region setupsHybrid and tiered topology designsLatency, RPO, and RTO considerations
